让机器人在有限感知下更早锁定目标，通过动态规划路径获取关键信息。
Active Sensing and Deferred-Decision Trajectory Optimization for Robust Target Identification
- 路径规划时主动选择能更快识别目标的位置，提升信息获取效率。
- 在距离相关感知不确定性下，识别成功率显著高于传统方法。
- 适合资源受限的移动传感系统，如无人机巡检、搜救机器人。
我们研究在资源受限条件下，移动感知系统需从有限候选集中识别出真实目标，同时保持对所有潜在目标的可达性。延迟决策轨迹优化（DDTO）通过计算抵达各目标的路径，尽可能延长路径重合阶段再分叉。本文提出主动感知DDTO（AS-DDTO），在规划目标中加入依赖轨迹的信息获取项，使重合路径偏向能提前识别目标的区域。该框架支持基于距离的感知的贝叶斯更新与保形候选集更新。我们推导出混合整数锥型重构形式，并提供递归可行性、信念集中性和固定时间覆盖性的保证。数值仿真显示，在距离相关感知不确定性与有限感知预算下，目标识别性能优于标准DDTO。
原文摘要 · Abstract (English)
We study trajectory optimization in mobile sensing systems that must identify which member of a finite candidate set is the true target, while maintaining reachability to all potential candidate targets, under resource constraints. Deferred-Decision Trajectory Optimization (DDTO) addresses this setting by computing trajectories that reach individual targets but remain coincident for as long as possible before separating toward different targets. We propose Active-Sensing DDTO (AS-DDTO), which extends DDTO by adding a trajectory-dependent information-acquisition term to the planning objective. The resulting planner maintains reachability to candidate targets while biasing the coincident portion of the trajectories toward regions that enable earlier target identification. The framework supports Bayesian updates and conformal candidate-set updates for distance-dependent sensing. We derive a mixed-integer conic reformulation and provide guarantees on recursive feasibility, belief concentration, and fixed-time coverage for the raw conformal candidate set. Numerical simulations show improved target identification compared with standard DDTO under distance-dependent sensing uncertainty and limited sensing budget.
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